Advances in Computational Pharmacology: AI and Machine Learning in Drug Discovery

Abstract

Computational pharmacology's incorporation of AI and ML has changed the game for contemporary drug discovery by opening up new avenues for the efficient and rapid creation of innovative medicines. By contrast, AI-driven approaches allow for more accurate identification of potential drug candidates, faster exploration of chemical space, and more accurate prediction of pharmacokinetic and pharmacodynamic characteristics, all while reducing the time, money, and strain on traditional drug discovery pipelines caused by high attrition rates. Virtual screening, target discovery, de novo drug creation, and drug repurposing have all been made possible by advancements in generative models, deep learning, and natural language processing. Machine learning algorithms that have been trained on massive chemical and biological datasets can optimize lead compounds, discover hidden connections connecting genotype, phenotype, and medication response, and forecast molecular interactions. Additionally, by customizing treatments to each patient, precision medicine is improved by integration with multi-omics data and real-world evidence. There are still significant obstacles to clinical translation, despite the revolutionary benefits, including issues with data quality, model interpretability, reproducibility, and regulatory acceptability. Also, we need to pay attention to ethical concerns, such as algorithmic prejudice and IP difficulties.